This integrative literature review examines PK-12 disaster schooling in US contexts, centering the experiences and knowledge systems of Black, Indigenous, and Latine (BIL) students, families, educators, and communities. Drawing on critical frameworks of disasters and schooling projects, and utilizing the PRISMA protocol, 844 articles were identified, with 18 meeting the inclusion criteria. Findings highlight how BIL communities navigated state neglect and school closures, resisted privatization and austerity, and mobilized deep-rooted traditions of activism, care, and collective survival. Schools emerged as contested spaces of trauma and dispossession, while also serving as sites of resistance and healing. The review highlights how disasters exacerbate pre-existing educational inequities and calls for inclusive, community-informed, and culturally sustaining school-based planning that recognizes and responds to BIL communities' knowledge as central to disaster preparedness, response, and educational transformation. Implications for policy, practice, and future research are discussed.
The emergence of generative artificial intelligence, such as ChatGPT and Doubao AI, is revolutionizing the traditional paradigms of writing among teenagers and how they engage with information regarding academia. While such innovations promise to enhance the speed and quality of writing, they bring forth various problematic implications for crucial areas within media literacy, particularly content evaluation and verification. This is a mixed-methods study, with data collected from 30 online questionnaires and seven semi-structured interviews conducted with 13- to 22-year-old students and educators. These results led to two main trends: first, students increasingly consume AI-generated content without the least bit of fact-checking or considering where it came from, and second, the psychological sense of dependence on AI—that is, many teenagers view AI as an absolute safety net for writing failure. In many instances, smooth-sounding responses from AI are quickly believed by students and then handed over as an easy pass on independent thinking and fact-checking. Such evidence comes through in reduced activity participation when new writing and critical thinking tasks are set by teachers. These findings situate the use of AI by teens within an emotional support, practical help framework. In conclusion, it offers some teaching suggestions for AI as a replacement and an assistant tool, insisting on the ground that AI requires critical literacy and responsible thought in its use.
ABSTRACT Background The growing volume of mammography screenings has created severe radiologist shortages, while standard First-In, First-Out (FIFO) reading queues fail to prioritize urgent or complex cases, delaying critical diagnoses. Objective This study introduces the Density and BI-RADS–Aware Triage and Report Generation (DB-ATRG) framework to fundamentally restructure mammography workflows by automating diagnostic text generation and enabling risk-based case prioritization. Methods Utilizing the Digital Mammography Dataset for Breast Cancer Diagnosis Research (DMID), we fine-tuned the 4-billion parameter MedGemma 1.5 vision-language model using Quantization and Low-Rank Adaptation (QLoRA). The extracted biomarkers drive a dual-phase triage algorithm that flags extremely dense breasts (ACR Category D) for supplemental screening and dynamically ranks remaining cases using a calculated Cumulative Urgency Score. The clinical impact of this triage workflow was evaluated against a standard FIFO queue using a simulated cohort of 100 mammography cases. Results DB-ATRG achieved significant improvements over the AMRG baseline in clinical text generation and classification, securing a ROUGE-L score of 0.8650, a METEOR score of 0.9001, and an ACR Density Accuracy of 0.7039. In clinical simulations, the optimized prioritization queue captured all high-risk malignancies (BI-RADS 4 and 5) within the first 20% of the reading workload, compared to just 40% in the random FIFO queue. This framework effectively accelerated the mean rank position of severe cases from 42.8 down to 3. Conclusion By accurately automating report generation and aggressively prioritizing severe cases, the DB-ATRG framework can drastically optimize clinical resource allocation and accelerate the time-to-diagnosis for the most vulnerable patients. Highlights Avision-language model (MedGemma 1.5 4B) is fine-tuned with QLoRA for automated mammography report generation, achieving ROUGE-L 0.8650 and METEOR 0.9001. A dual-phase Density and BI-RADS–Aware Triage algorithm restructures FIFO reading queues by clinical urgency. The triage system captures all high-risk cases (BI-RADS 4/5) within the first 20% of the worklist, versus 40% in standard FIFO. ACR breast density classification accuracy reaches 0.7039, enabling reliable density-based complexity filtering.
Uncrewed aerial vehicles (UAVs) are increasingly deployed in environments with limited visibility, such as smoke, dust, rain, or fog. In these conditions, conventional visual or range sensors provide little useful information to the operator. To address this, we present a new approach that converts aerodynamic in-ground effect (IGE) interactions into haptic feedback cues. These cues are derived from variations in UAV power without relying on sensors that may fail in low-visibility conditions. We describe the modeling, hardware, and software used to measure and render IGE-based haptic feedback, and evaluate participants' ability to discriminate heights in a user study. Results show users can distinguish different ground distances using only the haptic cues, highlighting the potential of IGEbased feedback in UAV teleoperation. Finally, we present a demonstration experiment where a user is flying the UAV blindly along a path through IGE haptic feedback, showcasing our system's ability to aid navigation under low-visibility conditions.
Introduction:Cardiac arrest is defined by a lack of central pulse, unresponsiveness, and apnea, indicating the termination of effective mechanical heart activity. Although rare in pediatrics, it results in poor outcomes. Results concerning the survival rates of pediatrics after cardiopulmonary resuscitation (CPR) in Jordan are absent, making this study crucial knowledge for implication of hospital protocol. Material/Method:A retrospective study was conducted at a tertiary hospital in Jordan. This study included 411 pediatric patients, aged over one day to under 12 years, who underwent CPR either during an emergency department (ED) visit or during hospitalization. The Mann-Whitney U-test, Chi-Square test, and Fisher's exact tests were used for analysis. Survival-associated factors were analyzed using univariate and multivariate logistic regression, and p < 0.05 was considered significant. Results:Patients were separated into a less than one year age group and a greater than one year age group, with similar gender distributions. Survival was significantly associated with age; the survivors were older than the non-survivors. Furthermore, the presence of any neurological manifestation was associated with a higher risk of mortality with an odds ratio of 3.97 (95% CI: 1.46-10.86, p = 0.007). After adjusting for all covariates, each 1% rise in oxygen saturation increased the adjusted odds ratio (AOR) of survival (AOR = 1.08, 95% CI 1.01-1.15; p = 0.031). In the same model, every additional minute of CPR sharply decreased the likelihood of survival (AOR = 0.38, 95% CI 0.21-0.72; p = 0.003). Conclusion:The survival rates after CPR in pediatrics were poor overall, suggesting a need for better pediatric CPR strategies and further studies. Many factors could affect the outcomes, most importantly, the duration of CPR and the oxygen saturation.
